- A
Enable pgvector index type to IVFFlat with a low number of lists
Why wrong: Index type and list count affect speed and recall trade-off, not fundamental relevance.
- B
Change the PostgreSQL instance to a larger instance type
Why wrong: Larger instances improve performance but not semantic relevance.
- C
Increase the number of provisioned Aurora read replicas
Why wrong: More replicas improve throughput, not search relevance.
- D
Switch the vector similarity metric to cosine similarity if using Euclidean, or adjust chunk size
Choosing the right similarity metric and chunking strategy directly impacts retrieval quality.
Question 362 of 1,000
mediumMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A developer is building a RAG application using…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer is building a RAG application using Amazon Aurora PostgreSQL with the pgvector extension. After ingesting documents, the vector search returns results that are not always the most relevant. What should the developer adjust to improve relevance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"always"Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Switch the vector similarity metric to cosine similarity if using Euclidean, or adjust chunk size
Option D is correct because relevance in vector search is primarily determined by the similarity metric and chunking strategy. Cosine similarity is often more effective than Euclidean for semantic similarity in high-dimensional spaces, and adjusting chunk size ensures that each vector captures a coherent semantic unit, directly improving retrieval relevance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable pgvector index type to IVFFlat with a low number of lists
Why it's wrong here
Index type and list count affect speed and recall trade-off, not fundamental relevance.
- ✗
Change the PostgreSQL instance to a larger instance type
Why it's wrong here
Larger instances improve performance but not semantic relevance.
- ✗
Increase the number of provisioned Aurora read replicas
Why it's wrong here
More replicas improve throughput, not search relevance.
- ✓
Switch the vector similarity metric to cosine similarity if using Euclidean, or adjust chunk size
Why this is correct
Choosing the right similarity metric and chunking strategy directly impacts retrieval quality.
Clue confirmation
The clue word "always" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall in AWS exam questions is to assume that scaling infrastructure (instance size, replicas) or index tuning (IVFFlat lists) can fix relevance issues, when the real cause is almost always the similarity metric or data preprocessing like chunk size.
Detailed technical explanation
How to think about this question
Cosine similarity measures the angle between vectors, making it robust to differences in magnitude, which is critical when embeddings from models like text-embedding-ada-002 are not normalized. Chunk size directly affects the semantic coherence of each vector; overly large chunks dilute meaning, while overly small chunks lose context, both degrading retrieval quality. In pgvector, the choice of distance operator (<-> for Euclidean, <=> for cosine) must match the metric used during index creation for optimal performance.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Switch the vector similarity metric to cosine similarity if using Euclidean, or adjust chunk size — Option D is correct because relevance in vector search is primarily determined by the similarity metric and chunking strategy. Cosine similarity is often more effective than Euclidean for semantic similarity in high-dimensional spaces, and adjusting chunk size ensures that each vector captures a coherent semantic unit, directly improving retrieval relevance.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "always". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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